Overview

Dataset statistics

Number of variables41
Number of observations207
Missing cells153
Missing cells (%)1.8%
Total size in memory66.4 KiB
Average record size in memory328.6 B

Variable types

Numeric19
Text22

Alerts

Status has constant value ""Constant
Clean Cup has constant value ""Constant
Sweetness has constant value ""Constant
Defects has constant value ""Constant
Mill has 3 (1.4%) missing valuesMissing
ICO Number has 132 (63.8%) missing valuesMissing
Variety has 6 (2.9%) missing valuesMissing
Processing Method has 5 (2.4%) missing valuesMissing
Unnamed: 0 has unique valuesUnique
ID has unique valuesUnique
Defects has 207 (100.0%) zerosZeros
Category One Defects has 193 (93.2%) zerosZeros
Quakers has 150 (72.5%) zerosZeros
Category Two Defects has 74 (35.7%) zerosZeros

Reproduction

Analysis started2023-08-03 16:36:24.673983
Analysis finished2023-08-03 16:36:25.357643
Duration0.68 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIQUE 

Distinct207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103
Minimum0
Maximum206
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:25.549033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.3
Q151.5
median103
Q3154.5
95-th percentile195.7
Maximum206
Range206
Interquartile range (IQR)103

Descriptive statistics

Standard deviation59.89991653
Coefficient of variation (CV)0.5815525876
Kurtosis-1.2
Mean103
Median Absolute Deviation (MAD)52
Skewness0
Sum21321
Variance3588
MonotonicityStrictly increasing
2023-08-03T10:36:25.655855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.5%
142 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
137 1
 
0.5%
138 1
 
0.5%
139 1
 
0.5%
Other values (197) 197
95.2%
ValueCountFrequency (%)
0 1
0.5%
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
ValueCountFrequency (%)
206 1
0.5%
205 1
0.5%
204 1
0.5%
203 1
0.5%
202 1
0.5%

ID
Real number (ℝ)

UNIQUE 

Distinct207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103
Minimum0
Maximum206
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:25.775557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.3
Q151.5
median103
Q3154.5
95-th percentile195.7
Maximum206
Range206
Interquartile range (IQR)103

Descriptive statistics

Standard deviation59.89991653
Coefficient of variation (CV)0.5815525876
Kurtosis-1.2
Mean103
Median Absolute Deviation (MAD)52
Skewness0
Sum21321
Variance3588
MonotonicityStrictly increasing
2023-08-03T10:36:25.889840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.5%
142 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
137 1
 
0.5%
138 1
 
0.5%
139 1
 
0.5%
Other values (197) 197
95.2%
ValueCountFrequency (%)
0 1
0.5%
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
ValueCountFrequency (%)
206 1
0.5%
205 1
0.5%
204 1
0.5%
203 1
0.5%
202 1
0.5%

Country of Origin
Text

Distinct22
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:26.031568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length8.29468599
Min length4

Characters and Unicode

Total characters1717
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowColombia
2nd rowTaiwan
3rd rowLaos
4th rowCosta Rica
5th rowColombia
ValueCountFrequency (%)
taiwan 61
24.4%
guatemala 21
 
8.4%
colombia 19
 
7.6%
honduras 13
 
5.2%
thailand 12
 
4.8%
ethiopia 11
 
4.4%
united 11
 
4.4%
brazil 10
 
4.0%
costa 8
 
3.2%
rica 8
 
3.2%
Other values (18) 76
30.4%
2023-08-03T10:36:26.263996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 376
21.9%
i 183
 
10.7%
n 127
 
7.4%
o 87
 
5.1%
l 82
 
4.8%
T 79
 
4.6%
w 66
 
3.8%
t 65
 
3.8%
e 60
 
3.5%
u 51
 
3.0%
Other values (34) 541
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1408
82.0%
Uppercase Letter 250
 
14.6%
Space Separator 43
 
2.5%
Other Punctuation 6
 
0.3%
Open Punctuation 5
 
0.3%
Close Punctuation 5
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 376
26.7%
i 183
13.0%
n 127
 
9.0%
o 87
 
6.2%
l 82
 
5.8%
w 66
 
4.7%
t 65
 
4.6%
e 60
 
4.3%
u 51
 
3.6%
d 50
 
3.6%
Other values (13) 261
18.5%
Uppercase Letter
ValueCountFrequency (%)
T 79
31.6%
C 27
 
10.8%
G 21
 
8.4%
E 18
 
7.2%
H 18
 
7.2%
R 14
 
5.6%
U 14
 
5.6%
S 12
 
4.8%
B 10
 
4.0%
N 7
 
2.8%
Other values (7) 30
 
12.0%
Space Separator
ValueCountFrequency (%)
43
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1658
96.6%
Common 59
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 376
22.7%
i 183
 
11.0%
n 127
 
7.7%
o 87
 
5.2%
l 82
 
4.9%
T 79
 
4.8%
w 66
 
4.0%
t 65
 
3.9%
e 60
 
3.6%
u 51
 
3.1%
Other values (30) 482
29.1%
Common
ValueCountFrequency (%)
43
72.9%
, 6
 
10.2%
( 5
 
8.5%
) 5
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1717
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 376
21.9%
i 183
 
10.7%
n 127
 
7.4%
o 87
 
5.1%
l 82
 
4.8%
T 79
 
4.6%
w 66
 
3.8%
t 65
 
3.8%
e 60
 
3.5%
u 51
 
3.0%
Other values (34) 541
31.5%

Farm Name
Text

Distinct172
Distinct (%)83.9%
Missing2
Missing (%)1.0%
Memory size1.7 KiB
2023-08-03T10:36:26.549110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length120
Median length32
Mean length14.74634146
Min length1

Characters and Unicode

Total characters3023
Distinct characters221
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)73.7%

Sample

1st rowFinca El Paraiso
2nd rowRoyal Bean Geisha Estate
3rd rowOKLAO coffee farms
4th rowLa Cumbre
5th rowFinca Santuario
ValueCountFrequency (%)
farm 20
 
4.1%
la 20
 
4.1%
finca 16
 
3.3%
san 14
 
2.9%
coffee 12
 
2.4%
doi 10
 
2.0%
de 9
 
1.8%
estate 8
 
1.6%
varias 7
 
1.4%
project 7
 
1.4%
Other values (273) 368
74.9%
2023-08-03T10:36:26.953782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 307
 
10.2%
284
 
9.4%
e 167
 
5.5%
i 153
 
5.1%
o 145
 
4.8%
n 137
 
4.5%
r 121
 
4.0%
t 89
 
2.9%
l 85
 
2.8%
s 82
 
2.7%
Other values (211) 1453
48.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1717
56.8%
Uppercase Letter 637
 
21.1%
Other Letter 344
 
11.4%
Space Separator 284
 
9.4%
Other Punctuation 13
 
0.4%
Decimal Number 12
 
0.4%
Open Punctuation 5
 
0.2%
Close Punctuation 5
 
0.2%
Dash Punctuation 4
 
0.1%
Control 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
園 40
 
11.6%
莊 34
 
9.9%
咖 31
 
9.0%
啡 31
 
9.0%
農 11
 
3.2%
場 8
 
2.3%
山 6
 
1.7%
香 5
 
1.5%
有 4
 
1.2%
機 4
 
1.2%
Other values (140) 170
49.4%
Lowercase Letter
ValueCountFrequency (%)
a 307
17.9%
e 167
9.7%
i 153
8.9%
o 145
8.4%
n 137
 
8.0%
r 121
 
7.0%
t 89
 
5.2%
l 85
 
5.0%
s 82
 
4.8%
c 71
 
4.1%
Other values (17) 360
21.0%
Uppercase Letter
ValueCountFrequency (%)
A 78
12.2%
C 57
 
8.9%
L 55
 
8.6%
F 51
 
8.0%
S 44
 
6.9%
M 38
 
6.0%
E 35
 
5.5%
D 33
 
5.2%
I 27
 
4.2%
N 27
 
4.2%
Other values (16) 192
30.1%
Decimal Number
ValueCountFrequency (%)
9 3
25.0%
1 3
25.0%
7 2
16.7%
5 2
16.7%
8 1
 
8.3%
2 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
, 4
30.8%
. 4
30.8%
' 2
15.4%
/ 2
15.4%
& 1
 
7.7%
Open Punctuation
ValueCountFrequency (%)
( 4
80.0%
（ 1
 
20.0%
Close Punctuation
ValueCountFrequency (%)
) 4
80.0%
） 1
 
20.0%
Space Separator
ValueCountFrequency (%)
284
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2354
77.9%
Han 344
 
11.4%
Common 325
 
10.8%

Most frequent character per script

Han
ValueCountFrequency (%)
園 40
 
11.6%
莊 34
 
9.9%
咖 31
 
9.0%
啡 31
 
9.0%
農 11
 
3.2%
場 8
 
2.3%
山 6
 
1.7%
香 5
 
1.5%
有 4
 
1.2%
機 4
 
1.2%
Other values (140) 170
49.4%
Latin
ValueCountFrequency (%)
a 307
 
13.0%
e 167
 
7.1%
i 153
 
6.5%
o 145
 
6.2%
n 137
 
5.8%
r 121
 
5.1%
t 89
 
3.8%
l 85
 
3.6%
s 82
 
3.5%
A 78
 
3.3%
Other values (43) 990
42.1%
Common
ValueCountFrequency (%)
284
87.4%
- 4
 
1.2%
, 4
 
1.2%
. 4
 
1.2%
( 4
 
1.2%
) 4
 
1.2%
9 3
 
0.9%
1 3
 
0.9%
7 2
 
0.6%
' 2
 
0.6%
Other values (8) 11
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2674
88.5%
CJK 344
 
11.4%
None 5
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 307
 
11.5%
284
 
10.6%
e 167
 
6.2%
i 153
 
5.7%
o 145
 
5.4%
n 137
 
5.1%
r 121
 
4.5%
t 89
 
3.3%
l 85
 
3.2%
s 82
 
3.1%
Other values (56) 1104
41.3%
CJK
ValueCountFrequency (%)
園 40
 
11.6%
莊 34
 
9.9%
咖 31
 
9.0%
啡 31
 
9.0%
農 11
 
3.2%
場 8
 
2.3%
山 6
 
1.7%
香 5
 
1.5%
有 4
 
1.2%
機 4
 
1.2%
Other values (140) 170
49.4%
None
ValueCountFrequency (%)
Ñ 1
20.0%
ó 1
20.0%
é 1
20.0%
） 1
20.0%
（ 1
20.0%

Lot Number
Text

Distinct187
Distinct (%)90.8%
Missing1
Missing (%)0.5%
Memory size1.7 KiB
2023-08-03T10:36:27.183179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length37
Mean length20.36407767
Min length1

Characters and Unicode

Total characters4195
Distinct characters90
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique180 ?
Unique (%)87.4%

Sample

1st rowCQU2022015
2nd rowThe 2022 Pacific Rim Coffee Summit,T037
3rd rowThe 2022 Pacific Rim Coffee Summit,LA01
4th rowCQU2022017
5th rowCQU2023002
ValueCountFrequency (%)
coffee 68
 
11.9%
pacific 42
 
7.4%
rim 42
 
7.4%
2022 40
 
7.0%
the 40
 
7.0%
taiwan 25
 
4.4%
evaluation 25
 
4.4%
specialty 25
 
4.4%
1 13
 
2.3%
21/22 8
 
1.4%
Other values (210) 242
42.5%
2023-08-03T10:36:27.514278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
364
 
8.7%
2 315
 
7.5%
0 259
 
6.2%
i 254
 
6.1%
e 244
 
5.8%
a 201
 
4.8%
f 192
 
4.6%
1 190
 
4.5%
o 132
 
3.1%
m 129
 
3.1%
Other values (80) 1915
45.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1862
44.4%
Decimal Number 1109
26.4%
Uppercase Letter 610
 
14.5%
Space Separator 364
 
8.7%
Other Punctuation 115
 
2.7%
Dash Punctuation 66
 
1.6%
Other Letter 51
 
1.2%
Close Punctuation 8
 
0.2%
Open Punctuation 8
 
0.2%
Final Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
次 3
 
5.9%
臺 3
 
5.9%
南 3
 
5.9%
市 3
 
5.9%
精 3
 
5.9%
品 3
 
5.9%
咖 3
 
5.9%
啡 3
 
5.9%
評 3
 
5.9%
鑑 3
 
5.9%
Other values (15) 21
41.2%
Uppercase Letter
ValueCountFrequency (%)
C 107
17.5%
T 94
15.4%
S 76
12.5%
P 54
8.9%
R 50
8.2%
A 47
7.7%
N 41
 
6.7%
E 30
 
4.9%
U 21
 
3.4%
Q 16
 
2.6%
Other values (14) 74
12.1%
Lowercase Letter
ValueCountFrequency (%)
i 254
13.6%
e 244
13.1%
a 201
10.8%
f 192
10.3%
o 132
7.1%
m 129
6.9%
t 121
 
6.5%
c 116
 
6.2%
n 82
 
4.4%
u 71
 
3.8%
Other values (12) 320
17.2%
Decimal Number
ValueCountFrequency (%)
2 315
28.4%
0 259
23.4%
1 190
17.1%
3 86
 
7.8%
5 55
 
5.0%
4 48
 
4.3%
7 47
 
4.2%
9 40
 
3.6%
8 36
 
3.2%
6 33
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/ 66
57.4%
, 43
37.4%
. 4
 
3.5%
: 2
 
1.7%
Space Separator
ValueCountFrequency (%)
364
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2472
58.9%
Common 1672
39.9%
Han 51
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 254
 
10.3%
e 244
 
9.9%
a 201
 
8.1%
f 192
 
7.8%
o 132
 
5.3%
m 129
 
5.2%
t 121
 
4.9%
c 116
 
4.7%
C 107
 
4.3%
T 94
 
3.8%
Other values (36) 882
35.7%
Han
ValueCountFrequency (%)
次 3
 
5.9%
臺 3
 
5.9%
南 3
 
5.9%
市 3
 
5.9%
精 3
 
5.9%
品 3
 
5.9%
咖 3
 
5.9%
啡 3
 
5.9%
評 3
 
5.9%
鑑 3
 
5.9%
Other values (15) 21
41.2%
Common
ValueCountFrequency (%)
364
21.8%
2 315
18.8%
0 259
15.5%
1 190
11.4%
3 86
 
5.1%
/ 66
 
3.9%
- 66
 
3.9%
5 55
 
3.3%
4 48
 
2.9%
7 47
 
2.8%
Other values (9) 176
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4141
98.7%
CJK 51
 
1.2%
Punctuation 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
364
 
8.8%
2 315
 
7.6%
0 259
 
6.3%
i 254
 
6.1%
e 244
 
5.9%
a 201
 
4.9%
f 192
 
4.6%
1 190
 
4.6%
o 132
 
3.2%
m 129
 
3.1%
Other values (53) 1861
44.9%
CJK
ValueCountFrequency (%)
次 3
 
5.9%
臺 3
 
5.9%
南 3
 
5.9%
市 3
 
5.9%
精 3
 
5.9%
品 3
 
5.9%
咖 3
 
5.9%
啡 3
 
5.9%
評 3
 
5.9%
鑑 3
 
5.9%
Other values (15) 21
41.2%
Punctuation
ValueCountFrequency (%)
’ 2
100.0%
None
ValueCountFrequency (%)
á 1
100.0%

Mill
Text

MISSING 

Distinct162
Distinct (%)79.4%
Missing3
Missing (%)1.4%
Memory size1.7 KiB
2023-08-03T10:36:27.784048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length120
Median length40
Mean length15.81862745
Min length2

Characters and Unicode

Total characters3227
Distinct characters227
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique140 ?
Unique (%)68.6%

Sample

1st rowFinca El Paraiso
2nd rowRoyal Bean Geisha Estate
3rd rowoklao coffee processing plant
4th rowLa Montana Tarrazu MIll
5th rowFinca Santuario
ValueCountFrequency (%)
mill 28
 
5.5%
coffee 27
 
5.3%
dry 20
 
3.9%
beneficio 15
 
2.9%
la 9
 
1.8%
el 8
 
1.6%
san 7
 
1.4%
gourmet 6
 
1.2%
salvador 6
 
1.2%
farm 6
 
1.2%
Other values (264) 380
74.2%
2023-08-03T10:36:28.185999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
305
 
9.5%
a 266
 
8.2%
e 192
 
5.9%
i 168
 
5.2%
o 159
 
4.9%
l 154
 
4.8%
r 131
 
4.1%
n 130
 
4.0%
C 79
 
2.4%
t 77
 
2.4%
Other values (217) 1566
48.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1808
56.0%
Uppercase Letter 705
 
21.8%
Other Letter 317
 
9.8%
Space Separator 306
 
9.5%
Other Punctuation 53
 
1.6%
Decimal Number 15
 
0.5%
Open Punctuation 8
 
0.2%
Close Punctuation 8
 
0.2%
Dash Punctuation 3
 
0.1%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
園 34
 
10.7%
莊 28
 
8.8%
啡 25
 
7.9%
咖 25
 
7.9%
農 11
 
3.5%
場 9
 
2.8%
機 8
 
2.5%
山 6
 
1.9%
香 5
 
1.6%
有 4
 
1.3%
Other values (139) 162
51.1%
Lowercase Letter
ValueCountFrequency (%)
a 266
14.7%
e 192
10.6%
i 168
9.3%
o 159
8.8%
l 154
 
8.5%
r 131
 
7.2%
n 130
 
7.2%
t 77
 
4.3%
c 76
 
4.2%
f 69
 
3.8%
Other values (22) 386
21.3%
Uppercase Letter
ValueCountFrequency (%)
C 79
 
11.2%
A 62
 
8.8%
E 57
 
8.1%
M 52
 
7.4%
S 51
 
7.2%
L 42
 
6.0%
I 42
 
6.0%
O 38
 
5.4%
D 38
 
5.4%
F 35
 
5.0%
Other values (15) 209
29.6%
Other Punctuation
ValueCountFrequency (%)
. 23
43.4%
, 20
37.7%
/ 5
 
9.4%
& 2
 
3.8%
、 2
 
3.8%
' 1
 
1.9%
Decimal Number
ValueCountFrequency (%)
5 4
26.7%
9 3
20.0%
1 3
20.0%
2 2
13.3%
7 2
13.3%
8 1
 
6.7%
Space Separator
ValueCountFrequency (%)
305
99.7%
　 1
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 7
87.5%
（ 1
 
12.5%
Close Punctuation
ValueCountFrequency (%)
) 7
87.5%
） 1
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2513
77.9%
Common 397
 
12.3%
Han 317
 
9.8%

Most frequent character per script

Han
ValueCountFrequency (%)
園 34
 
10.7%
莊 28
 
8.8%
啡 25
 
7.9%
咖 25
 
7.9%
農 11
 
3.5%
場 9
 
2.8%
機 8
 
2.5%
山 6
 
1.9%
香 5
 
1.6%
有 4
 
1.3%
Other values (139) 162
51.1%
Latin
ValueCountFrequency (%)
a 266
 
10.6%
e 192
 
7.6%
i 168
 
6.7%
o 159
 
6.3%
l 154
 
6.1%
r 131
 
5.2%
n 130
 
5.2%
C 79
 
3.1%
t 77
 
3.1%
c 76
 
3.0%
Other values (47) 1081
43.0%
Common
ValueCountFrequency (%)
305
76.8%
. 23
 
5.8%
, 20
 
5.0%
( 7
 
1.8%
) 7
 
1.8%
/ 5
 
1.3%
5 4
 
1.0%
9 3
 
0.8%
1 3
 
0.8%
- 3
 
0.8%
Other values (11) 17
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892
89.6%
CJK 317
 
9.8%
None 18
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
305
 
10.5%
a 266
 
9.2%
e 192
 
6.6%
i 168
 
5.8%
o 159
 
5.5%
l 154
 
5.3%
r 131
 
4.5%
n 130
 
4.5%
C 79
 
2.7%
t 77
 
2.7%
Other values (57) 1231
42.6%
CJK
ValueCountFrequency (%)
園 34
 
10.7%
莊 28
 
8.8%
啡 25
 
7.9%
咖 25
 
7.9%
農 11
 
3.5%
場 9
 
2.8%
機 8
 
2.5%
山 6
 
1.9%
香 5
 
1.6%
有 4
 
1.3%
Other values (139) 162
51.1%
None
ValueCountFrequency (%)
é 4
22.2%
ñ 3
16.7%
、 2
11.1%
á 2
11.1%
　 1
 
5.6%
è 1
 
5.6%
ì 1
 
5.6%
í 1
 
5.6%
） 1
 
5.6%
（ 1
 
5.6%

ICO Number
Text

MISSING 

Distinct67
Distinct (%)89.3%
Missing132
Missing (%)63.8%
Memory size1.7 KiB
2023-08-03T10:36:28.366434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length14
Mean length10.06666667
Min length3

Characters and Unicode

Total characters755
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)82.7%

Sample

1st row033/DE/503/002 and 033/DE/268/002
2nd row010/0296/600
3rd row010/0475/0207
4th rownon
5th rownon
ValueCountFrequency (%)
non 5
 
6.2%
3093 2
 
2.5%
11/441/50 2
 
2.5%
11/35 2
 
2.5%
9-392-35 2
 
2.5%
010 2
 
2.5%
0218/0065 2
 
2.5%
010/0891/00041 1
 
1.2%
2-237-05 1
 
1.2%
002-1894-0006 1
 
1.2%
Other values (60) 60
75.0%
2023-08-03T10:36:28.640012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 148
19.6%
1 141
18.7%
/ 77
10.2%
3 66
8.7%
- 58
 
7.7%
5 48
 
6.4%
2 42
 
5.6%
7 28
 
3.7%
4 28
 
3.7%
6 28
 
3.7%
Other values (17) 91
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581
77.0%
Other Punctuation 79
 
10.5%
Dash Punctuation 58
 
7.7%
Lowercase Letter 18
 
2.4%
Uppercase Letter 14
 
1.9%
Space Separator 5
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 148
25.5%
1 141
24.3%
3 66
11.4%
5 48
 
8.3%
2 42
 
7.2%
7 28
 
4.8%
4 28
 
4.8%
6 28
 
4.8%
9 27
 
4.6%
8 25
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
D 4
28.6%
E 4
28.6%
R 1
 
7.1%
V 1
 
7.1%
L 1
 
7.1%
I 1
 
7.1%
C 1
 
7.1%
O 1
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
n 11
61.1%
o 5
27.8%
a 1
 
5.6%
d 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
/ 77
97.5%
# 1
 
1.3%
: 1
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 723
95.8%
Latin 32
 
4.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 148
20.5%
1 141
19.5%
/ 77
10.7%
3 66
9.1%
- 58
 
8.0%
5 48
 
6.6%
2 42
 
5.8%
7 28
 
3.9%
4 28
 
3.9%
6 28
 
3.9%
Other values (5) 59
 
8.2%
Latin
ValueCountFrequency (%)
n 11
34.4%
o 5
15.6%
D 4
 
12.5%
E 4
 
12.5%
R 1
 
3.1%
V 1
 
3.1%
L 1
 
3.1%
a 1
 
3.1%
d 1
 
3.1%
I 1
 
3.1%
Other values (2) 2
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 148
19.6%
1 141
18.7%
/ 77
10.2%
3 66
8.7%
- 58
 
7.7%
5 48
 
6.4%
2 42
 
5.6%
7 28
 
3.7%
4 28
 
3.7%
6 28
 
3.7%
Other values (17) 91
12.1%

Company
Text

Distinct72
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:28.875249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length34
Mean length21.48792271
Min length6

Characters and Unicode

Total characters4448
Distinct characters80
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)21.3%

Sample

1st rowCoffee Quality Union
2nd rowTaiwan Coffee Laboratory
3rd rowTaiwan Coffee Laboratory
4th rowCoffee Quality Union
5th rowCoffee Quality Union
ValueCountFrequency (%)
coffee 110
 
17.9%
taiwan 51
 
8.3%
laboratory 51
 
8.3%
taiwu 25
 
4.1%
cooperative 25
 
4.1%
s.a 19
 
3.1%
quality 16
 
2.6%
union 15
 
2.4%
ltd 10
 
1.6%
cafe 10
 
1.6%
Other values (131) 281
45.8%
2023-08-03T10:36:29.233006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 423
 
9.5%
e 420
 
9.4%
o 410
 
9.2%
406
 
9.1%
f 249
 
5.6%
r 241
 
5.4%
i 228
 
5.1%
n 201
 
4.5%
C 198
 
4.5%
t 162
 
3.6%
Other values (70) 1510
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3066
68.9%
Uppercase Letter 843
 
19.0%
Space Separator 406
 
9.1%
Other Punctuation 77
 
1.7%
Other Letter 48
 
1.1%
Open Punctuation 4
 
0.1%
Close Punctuation 4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 423
13.8%
e 420
13.7%
o 410
13.4%
f 249
8.1%
r 241
7.9%
i 228
7.4%
n 201
 
6.6%
t 162
 
5.3%
w 83
 
2.7%
y 81
 
2.6%
Other values (19) 568
18.5%
Uppercase Letter
ValueCountFrequency (%)
C 198
23.5%
T 117
13.9%
L 93
11.0%
A 69
 
8.2%
S 47
 
5.6%
D 41
 
4.9%
M 36
 
4.3%
E 32
 
3.8%
I 30
 
3.6%
O 26
 
3.1%
Other values (16) 154
18.3%
Other Letter
ValueCountFrequency (%)
司 5
10.4%
公 5
10.4%
限 5
10.4%
有 5
10.4%
際 3
 
6.2%
國 3
 
6.2%
嶧 3
 
6.2%
宸 3
 
6.2%
份 2
 
4.2%
股 2
 
4.2%
Other values (8) 12
25.0%
Other Punctuation
ValueCountFrequency (%)
. 57
74.0%
, 17
 
22.1%
& 2
 
2.6%
/ 1
 
1.3%
Space Separator
ValueCountFrequency (%)
406
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3909
87.9%
Common 491
 
11.0%
Han 48
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 423
 
10.8%
e 420
 
10.7%
o 410
 
10.5%
f 249
 
6.4%
r 241
 
6.2%
i 228
 
5.8%
n 201
 
5.1%
C 198
 
5.1%
t 162
 
4.1%
T 117
 
3.0%
Other values (45) 1260
32.2%
Han
ValueCountFrequency (%)
司 5
10.4%
公 5
10.4%
限 5
10.4%
有 5
10.4%
際 3
 
6.2%
國 3
 
6.2%
嶧 3
 
6.2%
宸 3
 
6.2%
份 2
 
4.2%
股 2
 
4.2%
Other values (8) 12
25.0%
Common
ValueCountFrequency (%)
406
82.7%
. 57
 
11.6%
, 17
 
3.5%
( 4
 
0.8%
) 4
 
0.8%
& 2
 
0.4%
/ 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4390
98.7%
CJK 48
 
1.1%
None 10
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 423
 
9.6%
e 420
 
9.6%
o 410
 
9.3%
406
 
9.2%
f 249
 
5.7%
r 241
 
5.5%
i 228
 
5.2%
n 201
 
4.6%
C 198
 
4.5%
t 162
 
3.7%
Other values (49) 1452
33.1%
CJK
ValueCountFrequency (%)
司 5
10.4%
公 5
10.4%
限 5
10.4%
有 5
10.4%
際 3
 
6.2%
國 3
 
6.2%
嶧 3
 
6.2%
宸 3
 
6.2%
份 2
 
4.2%
股 2
 
4.2%
Other values (8) 12
25.0%
None
ValueCountFrequency (%)
é 5
50.0%
ñ 3
30.0%
ó 2
 
20.0%

Altitude
Text

Distinct97
Distinct (%)47.1%
Missing1
Missing (%)0.5%
Memory size1.7 KiB
2023-08-03T10:36:29.431803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length4.970873786
Min length3

Characters and Unicode

Total characters1024
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)31.6%

Sample

1st row1700-1930
2nd row1200
3rd row1300
4th row1900
5th row1850-2100
ValueCountFrequency (%)
1200 26
 
11.9%
1600 12
 
5.5%
1300 8
 
3.7%
1400 8
 
3.7%
1250 7
 
3.2%
1100 6
 
2.8%
1350 6
 
2.8%
1900 5
 
2.3%
5
 
2.3%
1450 5
 
2.3%
Other values (88) 130
59.6%
2023-08-03T10:36:29.744097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 409
39.9%
1 201
19.6%
5 84
 
8.2%
2 63
 
6.2%
3 49
 
4.8%
- 48
 
4.7%
6 42
 
4.1%
4 41
 
4.0%
8 32
 
3.1%
9 22
 
2.1%
Other values (4) 33
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 962
93.9%
Dash Punctuation 48
 
4.7%
Space Separator 12
 
1.2%
Math Symbol 1
 
0.1%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 409
42.5%
1 201
20.9%
5 84
 
8.7%
2 63
 
6.5%
3 49
 
5.1%
6 42
 
4.4%
4 41
 
4.3%
8 32
 
3.3%
9 22
 
2.3%
7 19
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1023
99.9%
Latin 1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 409
40.0%
1 201
19.6%
5 84
 
8.2%
2 63
 
6.2%
3 49
 
4.8%
- 48
 
4.7%
6 42
 
4.1%
4 41
 
4.0%
8 32
 
3.1%
9 22
 
2.2%
Other values (3) 32
 
3.1%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 409
39.9%
1 201
19.6%
5 84
 
8.2%
2 63
 
6.2%
3 49
 
4.8%
- 48
 
4.7%
6 42
 
4.1%
4 41
 
4.0%
8 32
 
3.1%
9 22
 
2.1%
Other values (4) 33
 
3.2%

Region
Text

Distinct120
Distinct (%)58.5%
Missing2
Missing (%)1.0%
Memory size1.7 KiB
2023-08-03T10:36:29.929111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length42
Mean length13.59512195
Min length3

Characters and Unicode

Total characters2787
Distinct characters92
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)38.5%

Sample

1st rowPiendamo,Cauca
2nd rowChiayi
3rd rowLaos Borofen Plateau
4th rowLos Santos,Tarrazu
5th rowPopayan,Cauca
ValueCountFrequency (%)
13
 
3.1%
chiayi 12
 
2.9%
yunlin 12
 
2.9%
township 11
 
2.6%
de 11
 
2.6%
新竹縣 11
 
2.6%
of 9
 
2.1%
north 7
 
1.7%
thailand 7
 
1.7%
苗栗縣 7
 
1.7%
Other values (160) 321
76.2%
2023-08-03T10:36:30.231710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 328
 
11.8%
216
 
7.8%
n 192
 
6.9%
i 181
 
6.5%
e 141
 
5.1%
o 128
 
4.6%
t 109
 
3.9%
u 104
 
3.7%
l 94
 
3.4%
r 91
 
3.3%
Other values (82) 1203
43.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1862
66.8%
Uppercase Letter 516
 
18.5%
Space Separator 216
 
7.8%
Other Letter 109
 
3.9%
Other Punctuation 68
 
2.4%
Dash Punctuation 10
 
0.4%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 328
17.6%
n 192
10.3%
i 181
9.7%
e 141
 
7.6%
o 128
 
6.9%
t 109
 
5.9%
u 104
 
5.6%
l 94
 
5.0%
r 91
 
4.9%
h 79
 
4.2%
Other values (20) 415
22.3%
Other Letter
ValueCountFrequency (%)
縣 18
16.5%
新 13
11.9%
竹 11
 
10.1%
市 7
 
6.4%
苗 7
 
6.4%
栗 7
 
6.4%
鄉 5
 
4.6%
桃 4
 
3.7%
園 4
 
3.7%
豐 3
 
2.8%
Other values (18) 30
27.5%
Uppercase Letter
ValueCountFrequency (%)
C 61
11.8%
A 48
 
9.3%
S 45
 
8.7%
T 42
 
8.1%
L 32
 
6.2%
M 32
 
6.2%
N 31
 
6.0%
I 27
 
5.2%
E 26
 
5.0%
R 20
 
3.9%
Other values (15) 152
29.5%
Other Punctuation
ValueCountFrequency (%)
, 54
79.4%
. 9
 
13.2%
' 2
 
2.9%
/ 2
 
2.9%
& 1
 
1.5%
Space Separator
ValueCountFrequency (%)
216
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2378
85.3%
Common 300
 
10.8%
Han 109
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 328
 
13.8%
n 192
 
8.1%
i 181
 
7.6%
e 141
 
5.9%
o 128
 
5.4%
t 109
 
4.6%
u 104
 
4.4%
l 94
 
4.0%
r 91
 
3.8%
h 79
 
3.3%
Other values (45) 931
39.2%
Han
ValueCountFrequency (%)
縣 18
16.5%
新 13
11.9%
竹 11
 
10.1%
市 7
 
6.4%
苗 7
 
6.4%
栗 7
 
6.4%
鄉 5
 
4.6%
桃 4
 
3.7%
園 4
 
3.7%
豐 3
 
2.8%
Other values (18) 30
27.5%
Common
ValueCountFrequency (%)
216
72.0%
, 54
 
18.0%
- 10
 
3.3%
. 9
 
3.0%
) 3
 
1.0%
( 3
 
1.0%
' 2
 
0.7%
/ 2
 
0.7%
& 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2672
95.9%
CJK 109
 
3.9%
None 6
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 328
 
12.3%
216
 
8.1%
n 192
 
7.2%
i 181
 
6.8%
e 141
 
5.3%
o 128
 
4.8%
t 109
 
4.1%
u 104
 
3.9%
l 94
 
3.5%
r 91
 
3.4%
Other values (50) 1088
40.7%
CJK
ValueCountFrequency (%)
縣 18
16.5%
新 13
11.9%
竹 11
 
10.1%
市 7
 
6.4%
苗 7
 
6.4%
栗 7
 
6.4%
鄉 5
 
4.6%
桃 4
 
3.7%
園 4
 
3.7%
豐 3
 
2.8%
Other values (18) 30
27.5%
None
ValueCountFrequency (%)
á 3
50.0%
ã 1
 
16.7%
â 1
 
16.7%
é 1
 
16.7%

Producer
Text

Distinct172
Distinct (%)83.5%
Missing1
Missing (%)0.5%
Memory size1.7 KiB
2023-08-03T10:36:30.493302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length39
Mean length14.5776699
Min length3

Characters and Unicode

Total characters3003
Distinct characters196
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique147 ?
Unique (%)71.4%

Sample

1st rowDiego Samuel Bermudez
2nd row曾福森
3rd rowWU TAO CHI
4th rowSanta Maria de Dota
5th rowCamilo Merizalde
ValueCountFrequency (%)
coffee 10
 
2.1%
de 10
 
2.1%
s.a 9
 
1.8%
doi 7
 
1.4%
development 7
 
1.4%
project 7
 
1.4%
tung 7
 
1.4%
limited 6
 
1.2%
varios 6
 
1.2%
maldonado 5
 
1.0%
Other values (305) 413
84.8%
2023-08-03T10:36:30.862979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
281
 
9.4%
a 252
 
8.4%
e 190
 
6.3%
o 188
 
6.3%
r 149
 
5.0%
i 141
 
4.7%
n 112
 
3.7%
t 97
 
3.2%
s 83
 
2.8%
u 83
 
2.8%
Other values (186) 1427
47.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1764
58.7%
Uppercase Letter 700
 
23.3%
Space Separator 281
 
9.4%
Other Letter 184
 
6.1%
Other Punctuation 50
 
1.7%
Dash Punctuation 9
 
0.3%
Open Punctuation 6
 
0.2%
Close Punctuation 6
 
0.2%
Decimal Number 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
陳 7
 
3.8%
榮 5
 
2.7%
明 5
 
2.7%
黃 4
 
2.2%
王 4
 
2.2%
李 4
 
2.2%
政 3
 
1.6%
吳 3
 
1.6%
志 3
 
1.6%
郭 3
 
1.6%
Other values (121) 143
77.7%
Lowercase Letter
ValueCountFrequency (%)
a 252
14.3%
e 190
10.8%
o 188
10.7%
r 149
 
8.4%
i 141
 
8.0%
n 112
 
6.3%
t 97
 
5.5%
s 83
 
4.7%
u 83
 
4.7%
l 82
 
4.6%
Other values (19) 387
21.9%
Uppercase Letter
ValueCountFrequency (%)
A 66
 
9.4%
C 60
 
8.6%
N 50
 
7.1%
S 49
 
7.0%
M 42
 
6.0%
D 41
 
5.9%
L 41
 
5.9%
E 40
 
5.7%
T 38
 
5.4%
O 31
 
4.4%
Other values (14) 242
34.6%
Other Punctuation
ValueCountFrequency (%)
. 31
62.0%
, 14
28.0%
& 4
 
8.0%
/ 1
 
2.0%
Open Punctuation
ValueCountFrequency (%)
( 5
83.3%
（ 1
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 5
83.3%
） 1
 
16.7%
Decimal Number
ValueCountFrequency (%)
0 2
66.7%
5 1
33.3%
Space Separator
ValueCountFrequency (%)
281
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2464
82.1%
Common 355
 
11.8%
Han 184
 
6.1%

Most frequent character per script

Han
ValueCountFrequency (%)
陳 7
 
3.8%
榮 5
 
2.7%
明 5
 
2.7%
黃 4
 
2.2%
王 4
 
2.2%
李 4
 
2.2%
政 3
 
1.6%
吳 3
 
1.6%
志 3
 
1.6%
郭 3
 
1.6%
Other values (121) 143
77.7%
Latin
ValueCountFrequency (%)
a 252
 
10.2%
e 190
 
7.7%
o 188
 
7.6%
r 149
 
6.0%
i 141
 
5.7%
n 112
 
4.5%
t 97
 
3.9%
s 83
 
3.4%
u 83
 
3.4%
l 82
 
3.3%
Other values (43) 1087
44.1%
Common
ValueCountFrequency (%)
281
79.2%
. 31
 
8.7%
, 14
 
3.9%
- 9
 
2.5%
( 5
 
1.4%
) 5
 
1.4%
& 4
 
1.1%
0 2
 
0.6%
） 1
 
0.3%
（ 1
 
0.3%
Other values (2) 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2814
93.7%
CJK 184
 
6.1%
None 5
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
281
 
10.0%
a 252
 
9.0%
e 190
 
6.8%
o 188
 
6.7%
r 149
 
5.3%
i 141
 
5.0%
n 112
 
4.0%
t 97
 
3.4%
s 83
 
2.9%
u 83
 
2.9%
Other values (50) 1238
44.0%
CJK
ValueCountFrequency (%)
陳 7
 
3.8%
榮 5
 
2.7%
明 5
 
2.7%
黃 4
 
2.2%
王 4
 
2.2%
李 4
 
2.2%
政 3
 
1.6%
吳 3
 
1.6%
志 3
 
1.6%
郭 3
 
1.6%
Other values (121) 143
77.7%
None
ValueCountFrequency (%)
） 1
20.0%
（ 1
20.0%
é 1
20.0%
ó 1
20.0%
í 1
20.0%

Number of Bags
Real number (ℝ)

Distinct55
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.4492754
Minimum1
Maximum2240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:30.973807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median14
Q3275
95-th percentile600
Maximum2240
Range2239
Interquartile range (IQR)274

Descriptive statistics

Standard deviation244.4848678
Coefficient of variation (CV)1.572762994
Kurtosis25.02356703
Mean155.4492754
Median Absolute Deviation (MAD)13
Skewness3.648140617
Sum32178
Variance59772.85057
MonotonicityNot monotonic
2023-08-03T10:36:31.078621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 62
30.0%
275 16
 
7.7%
320 14
 
6.8%
2 11
 
5.3%
600 10
 
4.8%
5 7
 
3.4%
3 6
 
2.9%
200 5
 
2.4%
300 4
 
1.9%
4 4
 
1.9%
Other values (45) 68
32.9%
ValueCountFrequency (%)
1 62
30.0%
2 11
 
5.3%
3 6
 
2.9%
4 4
 
1.9%
5 7
 
3.4%
ValueCountFrequency (%)
2240 1
0.5%
960 1
0.5%
635 1
0.5%
632 1
0.5%
620 2
1.0%

Bag Weight
Text

Distinct39
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:31.191365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.898550725
Min length4

Characters and Unicode

Total characters1014
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)7.2%

Sample

1st row35 kg
2nd row80 kg
3rd row25 kg
4th row22 kg
5th row24 kg
ValueCountFrequency (%)
kg 207
50.0%
30 39
 
9.4%
60 31
 
7.5%
69 25
 
6.0%
1 14
 
3.4%
15 10
 
2.4%
5 10
 
2.4%
2 8
 
1.9%
100 6
 
1.4%
50 6
 
1.4%
Other values (30) 58
 
14.0%
2023-08-03T10:36:31.411901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
207
20.4%
k 207
20.4%
g 207
20.4%
0 120
11.8%
6 60
 
5.9%
3 50
 
4.9%
1 47
 
4.6%
5 36
 
3.6%
9 30
 
3.0%
2 27
 
2.7%
Other values (3) 23
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 414
40.8%
Decimal Number 393
38.8%
Space Separator 207
20.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 120
30.5%
6 60
15.3%
3 50
12.7%
1 47
 
12.0%
5 36
 
9.2%
9 30
 
7.6%
2 27
 
6.9%
8 8
 
2.0%
4 8
 
2.0%
7 7
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
k 207
50.0%
g 207
50.0%
Space Separator
ValueCountFrequency (%)
207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 600
59.2%
Latin 414
40.8%

Most frequent character per script

Common
ValueCountFrequency (%)
207
34.5%
0 120
20.0%
6 60
 
10.0%
3 50
 
8.3%
1 47
 
7.8%
5 36
 
6.0%
9 30
 
5.0%
2 27
 
4.5%
8 8
 
1.3%
4 8
 
1.3%
Latin
ValueCountFrequency (%)
k 207
50.0%
g 207
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1014
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
207
20.4%
k 207
20.4%
g 207
20.4%
0 120
11.8%
6 60
 
5.9%
3 50
 
4.9%
1 47
 
4.6%
5 36
 
3.6%
9 30
 
3.0%
2 27
 
2.7%
Other values (3) 23
 
2.3%

In-Country Partner
Text

Distinct21
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:31.568272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length49
Mean length31.12560386
Min length21

Characters and Unicode

Total characters6443
Distinct characters65
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.9%

Sample

1st rowJapan Coffee Exchange
2nd rowTaiwan Coffee Laboratory 台灣咖啡研究室
3rd rowTaiwan Coffee Laboratory 台灣咖啡研究室
4th rowJapan Coffee Exchange
5th rowJapan Coffee Exchange
ValueCountFrequency (%)
coffee 155
18.3%
台灣咖啡研究室 83
 
9.8%
taiwan 83
 
9.8%
laboratory 83
 
9.8%
del 33
 
3.9%
café 29
 
3.4%
exchange 27
 
3.2%
japan 27
 
3.2%
association 24
 
2.8%
of 23
 
2.7%
Other values (53) 282
33.2%
2023-08-03T10:36:31.824080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 660
 
10.2%
642
 
10.0%
o 550
 
8.5%
e 500
 
7.8%
f 373
 
5.8%
n 287
 
4.5%
i 275
 
4.3%
r 255
 
4.0%
C 235
 
3.6%
t 217
 
3.4%
Other values (55) 2449
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4259
66.1%
Uppercase Letter 919
 
14.3%
Space Separator 642
 
10.0%
Other Letter 605
 
9.4%
Other Punctuation 10
 
0.2%
Open Punctuation 4
 
0.1%
Close Punctuation 4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 660
15.5%
o 550
12.9%
e 500
11.7%
f 373
8.8%
n 287
 
6.7%
i 275
 
6.5%
r 255
 
6.0%
t 217
 
5.1%
c 176
 
4.1%
l 141
 
3.3%
Other values (16) 825
19.4%
Uppercase Letter
ValueCountFrequency (%)
C 235
25.6%
A 126
13.7%
L 99
10.8%
T 98
10.7%
E 56
 
6.1%
I 38
 
4.1%
B 37
 
4.0%
N 33
 
3.6%
D 33
 
3.6%
S 31
 
3.4%
Other values (14) 133
14.5%
Other Letter
ValueCountFrequency (%)
咖 83
13.7%
研 83
13.7%
究 83
13.7%
室 83
13.7%
啡 83
13.7%
灣 83
13.7%
台 83
13.7%
國 6
 
1.0%
際 6
 
1.0%
嶧 6
 
1.0%
Space Separator
ValueCountFrequency (%)
642
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5178
80.4%
Common 660
 
10.2%
Han 605
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 660
 
12.7%
o 550
 
10.6%
e 500
 
9.7%
f 373
 
7.2%
n 287
 
5.5%
i 275
 
5.3%
r 255
 
4.9%
C 235
 
4.5%
t 217
 
4.2%
c 176
 
3.4%
Other values (40) 1650
31.9%
Han
ValueCountFrequency (%)
咖 83
13.7%
研 83
13.7%
究 83
13.7%
室 83
13.7%
啡 83
13.7%
灣 83
13.7%
台 83
13.7%
國 6
 
1.0%
際 6
 
1.0%
嶧 6
 
1.0%
Common
ValueCountFrequency (%)
642
97.3%
. 10
 
1.5%
( 4
 
0.6%
) 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5780
89.7%
CJK 605
 
9.4%
None 58
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 660
 
11.4%
642
 
11.1%
o 550
 
9.5%
e 500
 
8.7%
f 373
 
6.5%
n 287
 
5.0%
i 275
 
4.8%
r 255
 
4.4%
C 235
 
4.1%
t 217
 
3.8%
Other values (40) 1786
30.9%
CJK
ValueCountFrequency (%)
咖 83
13.7%
研 83
13.7%
究 83
13.7%
室 83
13.7%
啡 83
13.7%
灣 83
13.7%
台 83
13.7%
國 6
 
1.0%
際 6
 
1.0%
嶧 6
 
1.0%
None
ValueCountFrequency (%)
é 36
62.1%
ñ 10
 
17.2%
ó 8
 
13.8%
Ó 4
 
6.9%

Harvest Year
Text

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:31.921229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length8.835748792
Min length4

Characters and Unicode

Total characters1829
Distinct characters9
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row2021 / 2022
2nd row2021 / 2022
3rd row2021 / 2022
4th row2022
5th row2022
ValueCountFrequency (%)
2022 189
38.3%
143
29.0%
2021 112
22.7%
2023 45
 
9.1%
2018 2
 
0.4%
2017 1
 
0.2%
2019 1
 
0.2%
2023-08-03T10:36:32.121268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 885
48.4%
0 350
 
19.1%
286
 
15.6%
/ 143
 
7.8%
1 116
 
6.3%
3 45
 
2.5%
8 2
 
0.1%
7 1
 
0.1%
9 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
76.5%
Space Separator 286
 
15.6%
Other Punctuation 143
 
7.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 885
63.2%
0 350
 
25.0%
1 116
 
8.3%
3 45
 
3.2%
8 2
 
0.1%
7 1
 
0.1%
9 1
 
0.1%
Space Separator
ValueCountFrequency (%)
286
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 143
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 885
48.4%
0 350
 
19.1%
286
 
15.6%
/ 143
 
7.8%
1 116
 
6.3%
3 45
 
2.5%
8 2
 
0.1%
7 1
 
0.1%
9 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 885
48.4%
0 350
 
19.1%
286
 
15.6%
/ 143
 
7.8%
1 116
 
6.3%
3 45
 
2.5%
8 2
 
0.1%
7 1
 
0.1%
9 1
 
0.1%

Grading Date
Text

Distinct75
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:32.257554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length17.16908213
Min length14

Characters and Unicode

Total characters3554
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)20.8%

Sample

1st rowSeptember 21st, 2022
2nd rowNovember 15th, 2022
3rd rowNovember 15th, 2022
4th rowSeptember 21st, 2022
5th rowMarch 6th, 2023
ValueCountFrequency (%)
2022 132
21.3%
2023 75
 
12.1%
november 51
 
8.2%
15th 40
 
6.4%
january 31
 
5.0%
6th 29
 
4.7%
april 24
 
3.9%
december 16
 
2.6%
july 15
 
2.4%
june 14
 
2.3%
Other values (35) 194
31.2%
2023-08-03T10:36:32.496529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 604
17.0%
414
 
11.6%
0 223
 
6.3%
e 212
 
6.0%
t 212
 
6.0%
, 207
 
5.8%
h 187
 
5.3%
r 181
 
5.1%
1 110
 
3.1%
3 104
 
2.9%
Other values (29) 1100
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1545
43.5%
Decimal Number 1181
33.2%
Space Separator 414
 
11.6%
Other Punctuation 207
 
5.8%
Uppercase Letter 207
 
5.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 212
13.7%
t 212
13.7%
h 187
12.1%
r 181
11.7%
a 93
 
6.0%
b 93
 
6.0%
u 85
 
5.5%
m 78
 
5.0%
y 66
 
4.3%
o 57
 
3.7%
Other values (9) 281
18.2%
Decimal Number
ValueCountFrequency (%)
2 604
51.1%
0 223
 
18.9%
1 110
 
9.3%
3 104
 
8.8%
5 46
 
3.9%
6 36
 
3.0%
9 16
 
1.4%
7 15
 
1.3%
4 15
 
1.3%
8 12
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
J 60
29.0%
N 51
24.6%
A 32
15.5%
M 22
 
10.6%
D 16
 
7.7%
S 11
 
5.3%
F 9
 
4.3%
O 6
 
2.9%
Space Separator
ValueCountFrequency (%)
414
100.0%
Other Punctuation
ValueCountFrequency (%)
, 207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1802
50.7%
Latin 1752
49.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 212
12.1%
t 212
12.1%
h 187
 
10.7%
r 181
 
10.3%
a 93
 
5.3%
b 93
 
5.3%
u 85
 
4.9%
m 78
 
4.5%
y 66
 
3.8%
J 60
 
3.4%
Other values (17) 485
27.7%
Common
ValueCountFrequency (%)
2 604
33.5%
414
23.0%
0 223
 
12.4%
, 207
 
11.5%
1 110
 
6.1%
3 104
 
5.8%
5 46
 
2.6%
6 36
 
2.0%
9 16
 
0.9%
7 15
 
0.8%
Other values (2) 27
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3554
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 604
17.0%
414
 
11.6%
0 223
 
6.3%
e 212
 
6.0%
t 212
 
6.0%
, 207
 
5.8%
h 187
 
5.3%
r 181
 
5.1%
1 110
 
3.1%
3 104
 
2.9%
Other values (29) 1100
31.0%

Owner
Text

Distinct80
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:32.685684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length29
Mean length19.39130435
Min length3

Characters and Unicode

Total characters4014
Distinct characters110
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)24.6%

Sample

1st rowCoffee Quality Union
2nd rowTaiwan Coffee Laboratory 台灣咖啡研究室
3rd rowTaiwan Coffee Laboratory 台灣咖啡研究室
4th rowCoffee Quality Union
5th rowCoffee Quality Union
ValueCountFrequency (%)
coffee 53
 
8.9%
taiwan 30
 
5.0%
laboratory 30
 
5.0%
台灣咖啡研究室 30
 
5.0%
taiwu 25
 
4.2%
de 17
 
2.8%
quality 16
 
2.7%
union 15
 
2.5%
vàsquez 8
 
1.3%
yesica 8
 
1.3%
Other values (181) 366
61.2%
2023-08-03T10:36:33.001531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 396
 
9.9%
391
 
9.7%
e 314
 
7.8%
o 265
 
6.6%
n 213
 
5.3%
i 197
 
4.9%
r 161
 
4.0%
t 122
 
3.0%
f 116
 
2.9%
l 112
 
2.8%
Other values (100) 1727
43.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2604
64.9%
Uppercase Letter 729
 
18.2%
Space Separator 391
 
9.7%
Other Letter 259
 
6.5%
Other Punctuation 26
 
0.6%
Dash Punctuation 3
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
究 30
11.6%
台 30
11.6%
研 30
11.6%
咖 30
11.6%
灣 30
11.6%
室 30
11.6%
啡 30
11.6%
李 3
 
1.2%
宗 3
 
1.2%
郭 2
 
0.8%
Other values (36) 41
15.8%
Lowercase Letter
ValueCountFrequency (%)
a 396
15.2%
e 314
12.1%
o 265
10.2%
n 213
 
8.2%
i 197
 
7.6%
r 161
 
6.2%
t 122
 
4.7%
f 116
 
4.5%
l 112
 
4.3%
u 108
 
4.1%
Other values (21) 600
23.0%
Uppercase Letter
ValueCountFrequency (%)
C 99
13.6%
T 77
 
10.6%
L 62
 
8.5%
A 61
 
8.4%
S 35
 
4.8%
E 33
 
4.5%
D 32
 
4.4%
U 32
 
4.4%
M 31
 
4.3%
I 30
 
4.1%
Other values (16) 237
32.5%
Other Punctuation
ValueCountFrequency (%)
. 18
69.2%
, 7
 
26.9%
& 1
 
3.8%
Space Separator
ValueCountFrequency (%)
391
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3333
83.0%
Common 422
 
10.5%
Han 259
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 396
 
11.9%
e 314
 
9.4%
o 265
 
8.0%
n 213
 
6.4%
i 197
 
5.9%
r 161
 
4.8%
t 122
 
3.7%
f 116
 
3.5%
l 112
 
3.4%
u 108
 
3.2%
Other values (47) 1329
39.9%
Han
ValueCountFrequency (%)
究 30
11.6%
台 30
11.6%
研 30
11.6%
咖 30
11.6%
灣 30
11.6%
室 30
11.6%
啡 30
11.6%
李 3
 
1.2%
宗 3
 
1.2%
郭 2
 
0.8%
Other values (36) 41
15.8%
Common
ValueCountFrequency (%)
391
92.7%
. 18
 
4.3%
, 7
 
1.7%
- 3
 
0.7%
( 1
 
0.2%
& 1
 
0.2%
) 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3732
93.0%
CJK 259
 
6.5%
None 23
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 396
 
10.6%
391
 
10.5%
e 314
 
8.4%
o 265
 
7.1%
n 213
 
5.7%
i 197
 
5.3%
r 161
 
4.3%
t 122
 
3.3%
f 116
 
3.1%
l 112
 
3.0%
Other values (49) 1445
38.7%
CJK
ValueCountFrequency (%)
究 30
11.6%
台 30
11.6%
研 30
11.6%
咖 30
11.6%
灣 30
11.6%
室 30
11.6%
啡 30
11.6%
李 3
 
1.2%
宗 3
 
1.2%
郭 2
 
0.8%
Other values (36) 41
15.8%
None
ValueCountFrequency (%)
à 8
34.8%
ì 8
34.8%
é 3
 
13.0%
ñ 3
 
13.0%
ú 1
 
4.3%

Variety
Text

MISSING 

Distinct48
Distinct (%)23.9%
Missing6
Missing (%)2.9%
Memory size1.7 KiB
2023-08-03T10:36:33.166900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length33
Mean length8.587064677
Min length3

Characters and Unicode

Total characters1726
Distinct characters53
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)14.4%

Sample

1st rowCastillo
2nd rowGesha
3rd rowJava
4th rowGesha
5th rowRed Bourbon
ValueCountFrequency (%)
caturra 31
12.6%
gesha 28
11.3%
typica 27
10.9%
bourbon 25
 
10.1%
catuai 16
 
6.5%
catimor 14
 
5.7%
unknown 12
 
4.9%
ethiopian 9
 
3.6%
heirlooms 9
 
3.6%
sl34 9
 
3.6%
Other values (39) 67
27.1%
2023-08-03T10:36:33.597488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 229
 
13.3%
o 144
 
8.3%
r 136
 
7.9%
i 102
 
5.9%
u 101
 
5.9%
n 88
 
5.1%
t 83
 
4.8%
C 79
 
4.6%
e 57
 
3.3%
46
 
2.7%
Other values (43) 661
38.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1286
74.5%
Uppercase Letter 329
 
19.1%
Space Separator 46
 
2.7%
Decimal Number 38
 
2.2%
Other Punctuation 23
 
1.3%
Math Symbol 2
 
0.1%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 79
24.0%
T 34
10.3%
G 33
10.0%
B 29
 
8.8%
S 28
 
8.5%
L 21
 
6.4%
A 17
 
5.2%
E 14
 
4.3%
H 12
 
3.6%
M 12
 
3.6%
Other values (12) 50
15.2%
Lowercase Letter
ValueCountFrequency (%)
a 229
17.8%
o 144
11.2%
r 136
10.6%
i 102
 
7.9%
u 101
 
7.9%
n 88
 
6.8%
t 83
 
6.5%
e 57
 
4.4%
s 45
 
3.5%
h 41
 
3.2%
Other values (11) 260
20.2%
Decimal Number
ValueCountFrequency (%)
4 15
39.5%
3 11
28.9%
1 6
 
15.8%
2 3
 
7.9%
8 3
 
7.9%
Other Punctuation
ValueCountFrequency (%)
, 22
95.7%
& 1
 
4.3%
Space Separator
ValueCountFrequency (%)
46
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1615
93.6%
Common 111
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 229
14.2%
o 144
 
8.9%
r 136
 
8.4%
i 102
 
6.3%
u 101
 
6.3%
n 88
 
5.4%
t 83
 
5.1%
C 79
 
4.9%
e 57
 
3.5%
s 45
 
2.8%
Other values (33) 551
34.1%
Common
ValueCountFrequency (%)
46
41.4%
, 22
19.8%
4 15
 
13.5%
3 11
 
9.9%
1 6
 
5.4%
2 3
 
2.7%
8 3
 
2.7%
+ 2
 
1.8%
- 2
 
1.8%
& 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 229
 
13.3%
o 144
 
8.3%
r 136
 
7.9%
i 102
 
5.9%
u 101
 
5.9%
n 88
 
5.1%
t 83
 
4.8%
C 79
 
4.6%
e 57
 
3.3%
46
 
2.7%
Other values (43) 661
38.3%

Status
Text

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:33.768468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1863
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompleted
2nd rowCompleted
3rd rowCompleted
4th rowCompleted
5th rowCompleted
ValueCountFrequency (%)
completed 207
100.0%
2023-08-03T10:36:34.115309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 414
22.2%
C 207
11.1%
o 207
11.1%
m 207
11.1%
p 207
11.1%
l 207
11.1%
t 207
11.1%
d 207
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1656
88.9%
Uppercase Letter 207
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 414
25.0%
o 207
12.5%
m 207
12.5%
p 207
12.5%
l 207
12.5%
t 207
12.5%
d 207
12.5%
Uppercase Letter
ValueCountFrequency (%)
C 207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1863
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 414
22.2%
C 207
11.1%
o 207
11.1%
m 207
11.1%
p 207
11.1%
l 207
11.1%
t 207
11.1%
d 207
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1863
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 414
22.2%
C 207
11.1%
o 207
11.1%
m 207
11.1%
p 207
11.1%
l 207
11.1%
t 207
11.1%
d 207
11.1%

Processing Method
Text

MISSING 

Distinct10
Distinct (%)5.0%
Missing5
Missing (%)2.4%
Memory size1.7 KiB
2023-08-03T10:36:34.298163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length12
Mean length13.64356436
Min length11

Characters and Unicode

Total characters2756
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)3.5%

Sample

1st rowDouble Anaerobic Washed
2nd rowWashed / Wet
3rd rowSemi Washed
4th rowWashed / Wet
5th rowHoney,Mossto
ValueCountFrequency (%)
196
31.3%
washed 126
20.1%
wet 125
20.0%
natural 72
 
11.5%
dry 46
 
7.3%
pulped 25
 
4.0%
honey 25
 
4.0%
double 2
 
0.3%
maceration 1
 
0.2%
1000h 1
 
0.2%
Other values (7) 7
 
1.1%
2023-08-03T10:36:34.568361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
424
15.4%
e 308
11.2%
a 275
10.0%
W 251
9.1%
t 199
 
7.2%
/ 196
 
7.1%
h 152
 
5.5%
d 151
 
5.5%
s 128
 
4.6%
r 122
 
4.4%
Other values (28) 550
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1741
63.2%
Space Separator 424
 
15.4%
Uppercase Letter 389
 
14.1%
Other Punctuation 197
 
7.1%
Decimal Number 4
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 308
17.7%
a 275
15.8%
t 199
11.4%
h 152
8.7%
d 151
8.7%
s 128
7.4%
r 122
 
7.0%
l 101
 
5.8%
u 100
 
5.7%
y 72
 
4.1%
Other values (8) 133
7.6%
Uppercase Letter
ValueCountFrequency (%)
W 251
64.5%
D 49
 
12.6%
N 47
 
12.1%
P 25
 
6.4%
A 4
 
1.0%
M 3
 
0.8%
S 2
 
0.5%
H 2
 
0.5%
E 1
 
0.3%
V 1
 
0.3%
Other values (4) 4
 
1.0%
Other Punctuation
ValueCountFrequency (%)
/ 196
99.5%
, 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 3
75.0%
1 1
 
25.0%
Space Separator
ValueCountFrequency (%)
424
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2130
77.3%
Common 626
 
22.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 308
14.5%
a 275
12.9%
W 251
11.8%
t 199
9.3%
h 152
7.1%
d 151
7.1%
s 128
 
6.0%
r 122
 
5.7%
l 101
 
4.7%
u 100
 
4.7%
Other values (22) 343
16.1%
Common
ValueCountFrequency (%)
424
67.7%
/ 196
31.3%
0 3
 
0.5%
- 1
 
0.2%
1 1
 
0.2%
, 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
424
15.4%
e 308
11.2%
a 275
10.0%
W 251
9.1%
t 199
 
7.2%
/ 196
 
7.1%
h 152
 
5.5%
d 151
 
5.5%
s 128
 
4.6%
r 122
 
4.4%
Other values (28) 550
20.0%

Aroma
Real number (ℝ)

Distinct19
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.721062802
Minimum6.5
Maximum8.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:34.681526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.5
5-th percentile7.25
Q17.58
median7.67
Q37.92
95-th percentile8.17
Maximum8.58
Range2.08
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.287626428
Coefficient of variation (CV)0.03725218086
Kurtosis1.101152497
Mean7.721062802
Median Absolute Deviation (MAD)0.17
Skewness-0.1128019215
Sum1598.26
Variance0.08272896206
MonotonicityNot monotonic
2023-08-03T10:36:34.807846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7.67 37
17.9%
7.75 21
10.1%
7.58 21
10.1%
7.83 19
9.2%
8 16
7.7%
7.92 15
7.2%
7.5 14
 
6.8%
8.08 13
 
6.3%
7.42 12
 
5.8%
7.33 12
 
5.8%
Other values (9) 27
13.0%
ValueCountFrequency (%)
6.5 1
 
0.5%
7.08 1
 
0.5%
7.17 3
 
1.4%
7.25 7
3.4%
7.33 12
5.8%
ValueCountFrequency (%)
8.58 1
 
0.5%
8.5 1
 
0.5%
8.33 4
1.9%
8.25 2
 
1.0%
8.17 7
3.4%

Flavor
Real number (ℝ)

Distinct19
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7447343
Minimum6.75
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:34.917123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.75
5-th percentile7.25
Q17.58
median7.75
Q37.92
95-th percentile8.17
Maximum8.5
Range1.75
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.279612773
Coefficient of variation (CV)0.03610359791
Kurtosis0.4281609271
Mean7.7447343
Median Absolute Deviation (MAD)0.17
Skewness-0.2144002905
Sum1603.16
Variance0.07818330285
MonotonicityNot monotonic
2023-08-03T10:36:35.011739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7.92 28
13.5%
7.67 25
12.1%
7.75 23
11.1%
7.83 22
10.6%
8 19
9.2%
7.5 18
8.7%
7.58 16
7.7%
7.42 14
6.8%
8.08 11
 
5.3%
8.17 7
 
3.4%
Other values (9) 24
11.6%
ValueCountFrequency (%)
6.75 1
 
0.5%
7.08 2
 
1.0%
7.17 4
1.9%
7.25 7
3.4%
7.33 3
1.4%
ValueCountFrequency (%)
8.5 2
 
1.0%
8.42 1
 
0.5%
8.33 2
 
1.0%
8.25 2
 
1.0%
8.17 7
3.4%

Aftertaste
Real number (ℝ)

Distinct20
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.599758454
Minimum6.67
Maximum8.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:35.106350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.67
5-th percentile7.17
Q17.42
median7.58
Q37.75
95-th percentile8.08
Maximum8.42
Range1.75
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.2759105659
Coefficient of variation (CV)0.0363051757
Kurtosis0.8501680099
Mean7.599758454
Median Absolute Deviation (MAD)0.17
Skewness-0.1899351159
Sum1573.15
Variance0.0761266404
MonotonicityNot monotonic
2023-08-03T10:36:35.210015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
7.58 32
15.5%
7.42 24
11.6%
7.67 24
11.6%
7.75 22
10.6%
7.5 21
10.1%
7.83 18
8.7%
7.33 13
6.3%
7.25 11
 
5.3%
8 9
 
4.3%
7.92 8
 
3.9%
Other values (10) 25
12.1%
ValueCountFrequency (%)
6.67 1
0.5%
6.75 2
1.0%
6.92 1
0.5%
7 1
0.5%
7.08 2
1.0%
ValueCountFrequency (%)
8.42 1
 
0.5%
8.25 2
 
1.0%
8.17 2
 
1.0%
8.08 7
3.4%
8 9
4.3%

Acidity
Real number (ℝ)

Distinct19
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.690289855
Minimum6.83
Maximum8.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:35.336309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.83
5-th percentile7.274
Q17.5
median7.67
Q37.875
95-th percentile8.08
Maximum8.58
Range1.75
Interquartile range (IQR)0.375

Descriptive statistics

Standard deviation0.2595102053
Coefficient of variation (CV)0.03374517868
Kurtosis0.5812873082
Mean7.690289855
Median Absolute Deviation (MAD)0.17
Skewness-0.1110737515
Sum1591.89
Variance0.06734554664
MonotonicityNot monotonic
2023-08-03T10:36:35.438624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7.75 31
15.0%
7.92 26
12.6%
7.67 25
12.1%
7.5 23
11.1%
7.83 20
9.7%
7.58 19
9.2%
7.42 15
7.2%
8 14
6.8%
7.33 11
 
5.3%
8.17 5
 
2.4%
Other values (9) 18
8.7%
ValueCountFrequency (%)
6.83 1
 
0.5%
7 1
 
0.5%
7.08 2
1.0%
7.17 4
1.9%
7.25 3
1.4%
ValueCountFrequency (%)
8.58 1
 
0.5%
8.33 1
 
0.5%
8.25 2
 
1.0%
8.17 5
2.4%
8.08 3
1.4%

Body
Real number (ℝ)

Distinct17
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.640917874
Minimum6.83
Maximum8.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:35.551037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.83
5-th percentile7.25
Q17.5
median7.67
Q37.75
95-th percentile8
Maximum8.25
Range1.42
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.2334993982
Coefficient of variation (CV)0.03055907707
Kurtosis0.6625352783
Mean7.640917874
Median Absolute Deviation (MAD)0.16
Skewness-0.2961088392
Sum1581.67
Variance0.05452196895
MonotonicityNot monotonic
2023-08-03T10:36:35.650244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7.67 34
16.4%
7.58 32
15.5%
7.75 29
14.0%
7.5 22
10.6%
7.83 20
9.7%
7.42 17
8.2%
7.92 13
 
6.3%
8 11
 
5.3%
7.33 10
 
4.8%
7.08 4
 
1.9%
Other values (7) 15
7.2%
ValueCountFrequency (%)
6.83 1
 
0.5%
7 1
 
0.5%
7.08 4
1.9%
7.17 4
1.9%
7.25 3
1.4%
ValueCountFrequency (%)
8.25 2
 
1.0%
8.17 2
 
1.0%
8.08 2
 
1.0%
8 11
5.3%
7.92 13
6.3%

Balance
Real number (ℝ)

Distinct18
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.644057971
Minimum6.67
Maximum8.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:35.741438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.67
5-th percentile7.194
Q17.5
median7.67
Q37.79
95-th percentile8
Maximum8.42
Range1.75
Interquartile range (IQR)0.29

Descriptive statistics

Standard deviation0.2562991966
Coefficient of variation (CV)0.03352920629
Kurtosis0.8816369541
Mean7.644057971
Median Absolute Deviation (MAD)0.17
Skewness-0.2265742448
Sum1582.32
Variance0.06568927818
MonotonicityNot monotonic
2023-08-03T10:36:35.833332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
7.67 33
15.9%
7.75 32
15.5%
7.5 26
12.6%
7.58 20
9.7%
7.83 18
8.7%
7.42 18
8.7%
8 13
 
6.3%
7.92 12
 
5.8%
7.33 10
 
4.8%
7.25 5
 
2.4%
Other values (8) 20
9.7%
ValueCountFrequency (%)
6.67 1
 
0.5%
7 2
 
1.0%
7.08 3
1.4%
7.17 5
2.4%
7.25 5
2.4%
ValueCountFrequency (%)
8.42 1
 
0.5%
8.25 2
 
1.0%
8.17 4
 
1.9%
8.08 2
 
1.0%
8 13
6.3%

Uniformity
Real number (ℝ)

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.990338164
Minimum8.67
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:35.921647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.67
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range1.33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1033064141
Coefficient of variation (CV)0.01034063236
Kurtosis138.655648
Mean9.990338164
Median Absolute Deviation (MAD)0
Skewness-11.51281517
Sum2068
Variance0.01067221519
MonotonicityNot monotonic
2023-08-03T10:36:36.011881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
10 205
99.0%
9.33 1
 
0.5%
8.67 1
 
0.5%
ValueCountFrequency (%)
8.67 1
 
0.5%
9.33 1
 
0.5%
10 205
99.0%
ValueCountFrequency (%)
10 205
99.0%
9.33 1
 
0.5%
8.67 1
 
0.5%

Clean Cup
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10
Minimum10
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:36.090373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean10
Median Absolute Deviation (MAD)0
Skewness0
Sum2070
Variance0
MonotonicityIncreasing
2023-08-03T10:36:36.165291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
10 207
100.0%
ValueCountFrequency (%)
10 207
100.0%
ValueCountFrequency (%)
10 207
100.0%

Sweetness
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10
Minimum10
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:36.242487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean10
Median Absolute Deviation (MAD)0
Skewness0
Sum2070
Variance0
MonotonicityIncreasing
2023-08-03T10:36:36.319746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
10 207
100.0%
ValueCountFrequency (%)
10 207
100.0%
ValueCountFrequency (%)
10 207
100.0%

Overall
Real number (ℝ)

Distinct21
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.676811594
Minimum6.67
Maximum8.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:36.400124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.67
5-th percentile7.17
Q17.5
median7.67
Q37.92
95-th percentile8.143
Maximum8.58
Range1.91
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.3063589466
Coefficient of variation (CV)0.03990705553
Kurtosis0.5603046368
Mean7.676811594
Median Absolute Deviation (MAD)0.25
Skewness-0.06983365005
Sum1589.1
Variance0.09385580414
MonotonicityNot monotonic
2023-08-03T10:36:36.495578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
7.75 28
13.5%
7.67 20
9.7%
7.92 20
9.7%
7.83 20
9.7%
7.5 20
9.7%
8 18
8.7%
7.42 17
8.2%
7.33 16
7.7%
7.58 15
7.2%
7.17 7
 
3.4%
Other values (11) 26
12.6%
ValueCountFrequency (%)
6.67 1
 
0.5%
6.83 1
 
0.5%
7 3
1.4%
7.08 2
 
1.0%
7.17 7
3.4%
ValueCountFrequency (%)
8.58 2
 
1.0%
8.5 1
 
0.5%
8.33 1
 
0.5%
8.25 5
2.4%
8.17 2
 
1.0%

Defects
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros207
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:36.581272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-08-03T10:36:36.654434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 207
100.0%
ValueCountFrequency (%)
0 207
100.0%
ValueCountFrequency (%)
0 207
100.0%

Total Cup Points
Real number (ℝ)

Distinct81
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.70657005
Minimum78
Maximum89.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:36.753640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile80.599
Q182.58
median83.75
Q384.83
95-th percentile86.25
Maximum89.33
Range11.33
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.730417011
Coefficient of variation (CV)0.02067241568
Kurtosis0.7007194004
Mean83.70657005
Median Absolute Deviation (MAD)1.17
Skewness-0.2136670808
Sum17327.26
Variance2.994343033
MonotonicityDecreasing
2023-08-03T10:36:36.867181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.33 8
 
3.9%
82.5 7
 
3.4%
83.25 6
 
2.9%
83.83 6
 
2.9%
84 6
 
2.9%
84.58 6
 
2.9%
83.67 5
 
2.4%
85 5
 
2.4%
84.33 5
 
2.4%
82 5
 
2.4%
Other values (71) 148
71.5%
ValueCountFrequency (%)
78 1
0.5%
78.08 1
0.5%
79.67 1
0.5%
80 1
0.5%
80.08 1
0.5%
ValueCountFrequency (%)
89.33 1
0.5%
87.58 1
0.5%
87.42 1
0.5%
87.17 1
0.5%
87.08 1
0.5%

Moisture Percentage
Real number (ℝ)

Distinct46
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.7352657
Minimum0
Maximum13.5
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:36.974094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.1
Q110.1
median10.8
Q311.5
95-th percentile12.17
Maximum13.5
Range13.5
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.247468427
Coefficient of variation (CV)0.116202846
Kurtosis25.66450387
Mean10.7352657
Median Absolute Deviation (MAD)0.7
Skewness-3.107197938
Sum2222.2
Variance1.556177478
MonotonicityNot monotonic
2023-08-03T10:36:37.097681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
11.8 13
 
6.3%
11.3 13
 
6.3%
11.6 13
 
6.3%
10 11
 
5.3%
10.6 11
 
5.3%
11.2 9
 
4.3%
11 9
 
4.3%
10.5 8
 
3.9%
10.4 7
 
3.4%
11.5 7
 
3.4%
Other values (36) 106
51.2%
ValueCountFrequency (%)
0 1
 
0.5%
8.1 1
 
0.5%
8.2 1
 
0.5%
8.4 3
1.4%
8.5 1
 
0.5%
ValueCountFrequency (%)
13.5 1
0.5%
13.3 2
1.0%
13.1 2
1.0%
13 1
0.5%
12.5 1
0.5%

Category One Defects
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1352657005
Minimum0
Maximum5
Zeros193
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:37.189511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5920695986
Coefficient of variation (CV)4.377085961
Kurtosis34.14499698
Mean0.1352657005
Median Absolute Deviation (MAD)0
Skewness5.483569418
Sum28
Variance0.3505464096
MonotonicityNot monotonic
2023-08-03T10:36:37.270787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 193
93.2%
1 6
 
2.9%
2 5
 
2.4%
3 1
 
0.5%
4 1
 
0.5%
5 1
 
0.5%
ValueCountFrequency (%)
0 193
93.2%
1 6
 
2.9%
2 5
 
2.4%
3 1
 
0.5%
4 1
 
0.5%
ValueCountFrequency (%)
5 1
 
0.5%
4 1
 
0.5%
3 1
 
0.5%
2 5
2.4%
1 6
2.9%

Quakers
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.690821256
Minimum0
Maximum12
Zeros150
Zeros (%)72.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:37.351539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.686917912
Coefficient of variation (CV)2.441902152
Kurtosis18.4743784
Mean0.690821256
Median Absolute Deviation (MAD)0
Skewness3.945370685
Sum143
Variance2.845692041
MonotonicityNot monotonic
2023-08-03T10:36:37.452514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 150
72.5%
1 25
 
12.1%
2 16
 
7.7%
3 8
 
3.9%
5 2
 
1.0%
7 1
 
0.5%
8 1
 
0.5%
6 1
 
0.5%
10 1
 
0.5%
9 1
 
0.5%
ValueCountFrequency (%)
0 150
72.5%
1 25
 
12.1%
2 16
 
7.7%
3 8
 
3.9%
5 2
 
1.0%
ValueCountFrequency (%)
12 1
0.5%
10 1
0.5%
9 1
0.5%
8 1
0.5%
7 1
0.5%

Color
Text

Distinct12
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:37.561530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length7.516908213
Min length5

Characters and Unicode

Total characters1556
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.4%

Sample

1st rowgreen
2nd rowblue-green
3rd rowyellowish
4th rowgreen
5th rowyellow-green
ValueCountFrequency (%)
green 107
48.9%
greenish 36
 
16.4%
bluish-green 21
 
9.6%
blue-green 12
 
5.5%
yellow 12
 
5.5%
yellow-green 10
 
4.6%
brownish 9
 
4.1%
pale 6
 
2.7%
yellowish 4
 
1.8%
browish-green 1
 
0.5%
2023-08-03T10:36:37.793916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 421
27.1%
r 198
12.7%
n 197
12.7%
g 188
12.1%
l 93
 
6.0%
s 71
 
4.6%
h 71
 
4.6%
i 71
 
4.6%
- 46
 
3.0%
b 43
 
2.8%
Other values (7) 157
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1498
96.3%
Dash Punctuation 46
 
3.0%
Space Separator 12
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 421
28.1%
r 198
13.2%
n 197
13.2%
g 188
12.6%
l 93
 
6.2%
s 71
 
4.7%
h 71
 
4.7%
i 71
 
4.7%
b 43
 
2.9%
o 37
 
2.5%
Other values (5) 108
 
7.2%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1498
96.3%
Common 58
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 421
28.1%
r 198
13.2%
n 197
13.2%
g 188
12.6%
l 93
 
6.2%
s 71
 
4.7%
h 71
 
4.7%
i 71
 
4.7%
b 43
 
2.9%
o 37
 
2.5%
Other values (5) 108
 
7.2%
Common
ValueCountFrequency (%)
- 46
79.3%
12
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1556
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 421
27.1%
r 198
12.7%
n 197
12.7%
g 188
12.1%
l 93
 
6.0%
s 71
 
4.6%
h 71
 
4.6%
i 71
 
4.6%
- 46
 
3.0%
b 43
 
2.8%
Other values (7) 157
 
10.1%

Category Two Defects
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.251207729
Minimum0
Maximum16
Zeros74
Zeros (%)35.7%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:37.897531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile8.7
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.950182813
Coefficient of variation (CV)1.310488932
Kurtosis4.297128302
Mean2.251207729
Median Absolute Deviation (MAD)1
Skewness1.992888435
Sum466
Variance8.703578631
MonotonicityNot monotonic
2023-08-03T10:36:37.990323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 74
35.7%
2 37
17.9%
1 34
16.4%
3 16
 
7.7%
5 13
 
6.3%
4 13
 
6.3%
7 5
 
2.4%
8 4
 
1.9%
12 3
 
1.4%
11 3
 
1.4%
Other values (4) 5
 
2.4%
ValueCountFrequency (%)
0 74
35.7%
1 34
16.4%
2 37
17.9%
3 16
 
7.7%
4 13
 
6.3%
ValueCountFrequency (%)
16 1
 
0.5%
13 1
 
0.5%
12 3
1.4%
11 3
1.4%
10 1
 
0.5%

Expiration
Text

Distinct75
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:38.121171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length17.16425121
Min length14

Characters and Unicode

Total characters3553
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)20.8%

Sample

1st rowSeptember 21st, 2023
2nd rowNovember 15th, 2023
3rd rowNovember 15th, 2023
4th rowSeptember 21st, 2023
5th rowMarch 5th, 2024
ValueCountFrequency (%)
2023 132
21.3%
2024 75
 
12.1%
november 51
 
8.2%
15th 40
 
6.4%
6th 32
 
5.2%
january 31
 
5.0%
april 24
 
3.9%
december 16
 
2.6%
july 15
 
2.4%
june 14
 
2.3%
Other values (35) 191
30.8%
2023-08-03T10:36:38.362578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 471
13.3%
414
 
11.7%
0 221
 
6.2%
t 213
 
6.0%
e 212
 
6.0%
, 207
 
5.8%
h 186
 
5.2%
r 180
 
5.1%
3 160
 
4.5%
1 112
 
3.2%
Other values (29) 1177
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1545
43.5%
Decimal Number 1180
33.2%
Space Separator 414
 
11.7%
Other Punctuation 207
 
5.8%
Uppercase Letter 207
 
5.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 213
13.8%
e 212
13.7%
h 186
12.0%
r 180
11.7%
b 93
 
6.0%
a 93
 
6.0%
u 85
 
5.5%
m 78
 
5.0%
y 66
 
4.3%
o 57
 
3.7%
Other values (9) 282
18.3%
Decimal Number
ValueCountFrequency (%)
2 471
39.9%
0 221
18.7%
3 160
 
13.6%
1 112
 
9.5%
4 94
 
8.0%
5 45
 
3.8%
6 42
 
3.6%
9 13
 
1.1%
8 13
 
1.1%
7 9
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
J 60
29.0%
N 51
24.6%
A 32
15.5%
M 22
 
10.6%
D 16
 
7.7%
S 11
 
5.3%
F 9
 
4.3%
O 6
 
2.9%
Space Separator
ValueCountFrequency (%)
414
100.0%
Other Punctuation
ValueCountFrequency (%)
, 207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1801
50.7%
Latin 1752
49.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 213
12.2%
e 212
12.1%
h 186
 
10.6%
r 180
 
10.3%
b 93
 
5.3%
a 93
 
5.3%
u 85
 
4.9%
m 78
 
4.5%
y 66
 
3.8%
J 60
 
3.4%
Other values (17) 486
27.7%
Common
ValueCountFrequency (%)
2 471
26.2%
414
23.0%
0 221
12.3%
, 207
11.5%
3 160
 
8.9%
1 112
 
6.2%
4 94
 
5.2%
5 45
 
2.5%
6 42
 
2.3%
9 13
 
0.7%
Other values (2) 22
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3553
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 471
13.3%
414
 
11.7%
0 221
 
6.2%
t 213
 
6.0%
e 212
 
6.0%
, 207
 
5.8%
h 186
 
5.2%
r 180
 
5.1%
3 160
 
4.5%
1 112
 
3.2%
Other values (29) 1177
33.1%

Certification Body
Text

Distinct21
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:38.534265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length49
Mean length31.12560386
Min length21

Characters and Unicode

Total characters6443
Distinct characters65
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.9%

Sample

1st rowJapan Coffee Exchange
2nd rowTaiwan Coffee Laboratory 台灣咖啡研究室
3rd rowTaiwan Coffee Laboratory 台灣咖啡研究室
4th rowJapan Coffee Exchange
5th rowJapan Coffee Exchange
ValueCountFrequency (%)
coffee 155
18.3%
台灣咖啡研究室 83
 
9.8%
taiwan 83
 
9.8%
laboratory 83
 
9.8%
del 33
 
3.9%
café 29
 
3.4%
exchange 27
 
3.2%
japan 27
 
3.2%
association 24
 
2.8%
of 23
 
2.7%
Other values (53) 282
33.2%
2023-08-03T10:36:38.791744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 660
 
10.2%
642
 
10.0%
o 550
 
8.5%
e 500
 
7.8%
f 373
 
5.8%
n 287
 
4.5%
i 275
 
4.3%
r 255
 
4.0%
C 235
 
3.6%
t 217
 
3.4%
Other values (55) 2449
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4259
66.1%
Uppercase Letter 919
 
14.3%
Space Separator 642
 
10.0%
Other Letter 605
 
9.4%
Other Punctuation 10
 
0.2%
Open Punctuation 4
 
0.1%
Close Punctuation 4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 660
15.5%
o 550
12.9%
e 500
11.7%
f 373
8.8%
n 287
 
6.7%
i 275
 
6.5%
r 255
 
6.0%
t 217
 
5.1%
c 176
 
4.1%
l 141
 
3.3%
Other values (16) 825
19.4%
Uppercase Letter
ValueCountFrequency (%)
C 235
25.6%
A 126
13.7%
L 99
10.8%
T 98
10.7%
E 56
 
6.1%
I 38
 
4.1%
B 37
 
4.0%
N 33
 
3.6%
D 33
 
3.6%
S 31
 
3.4%
Other values (14) 133
14.5%
Other Letter
ValueCountFrequency (%)
咖 83
13.7%
研 83
13.7%
究 83
13.7%
室 83
13.7%
啡 83
13.7%
灣 83
13.7%
台 83
13.7%
國 6
 
1.0%
際 6
 
1.0%
嶧 6
 
1.0%
Space Separator
ValueCountFrequency (%)
642
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5178
80.4%
Common 660
 
10.2%
Han 605
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 660
 
12.7%
o 550
 
10.6%
e 500
 
9.7%
f 373
 
7.2%
n 287
 
5.5%
i 275
 
5.3%
r 255
 
4.9%
C 235
 
4.5%
t 217
 
4.2%
c 176
 
3.4%
Other values (40) 1650
31.9%
Han
ValueCountFrequency (%)
咖 83
13.7%
研 83
13.7%
究 83
13.7%
室 83
13.7%
啡 83
13.7%
灣 83
13.7%
台 83
13.7%
國 6
 
1.0%
際 6
 
1.0%
嶧 6
 
1.0%
Common
ValueCountFrequency (%)
642
97.3%
. 10
 
1.5%
( 4
 
0.6%
) 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5780
89.7%
CJK 605
 
9.4%
None 58
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 660
 
11.4%
642
 
11.1%
o 550
 
9.5%
e 500
 
8.7%
f 373
 
6.5%
n 287
 
5.0%
i 275
 
4.8%
r 255
 
4.4%
C 235
 
4.1%
t 217
 
3.8%
Other values (40) 1786
30.9%
CJK
ValueCountFrequency (%)
咖 83
13.7%
研 83
13.7%
究 83
13.7%
室 83
13.7%
啡 83
13.7%
灣 83
13.7%
台 83
13.7%
國 6
 
1.0%
際 6
 
1.0%
嶧 6
 
1.0%
None
ValueCountFrequency (%)
é 36
62.1%
ñ 10
 
17.2%
ó 8
 
13.8%
Ó 4
 
6.9%

Certification Address
Text

Distinct21
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:39.044283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length149
Median length133
Mean length77.97101449
Min length27

Characters and Unicode

Total characters16140
Distinct characters91
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.9%

Sample

1st row〒413-0002 静岡県熱海市伊豆山１１７３−５８ 1173-58 Izusan, Atami, Shizuoka, 413-0002 JAPAN
2nd rowQAHWAH CO., LTD 4F, No. 225, Sec. 3, Beixin Rd., Xindian Dist. New Taipei City, Taiwan
3rd rowQAHWAH CO., LTD 4F, No. 225, Sec. 3, Beixin Rd., Xindian Dist. New Taipei City, Taiwan
4th row〒413-0002 静岡県熱海市伊豆山１１７３−５８ 1173-58 Izusan, Atami, Shizuoka, 413-0002 JAPAN
5th row〒413-0002 静岡県熱海市伊豆山１１７３−５８ 1173-58 Izusan, Atami, Shizuoka, 413-0002 JAPAN
ValueCountFrequency (%)
city 98
 
3.6%
rd 92
 
3.4%
taiwan 89
 
3.3%
no 89
 
3.3%
3 84
 
3.1%
qahwah 83
 
3.0%
xindian 83
 
3.0%
taipei 83
 
3.0%
new 83
 
3.0%
dist 83
 
3.0%
Other values (194) 1866
68.3%
2023-08-03T10:36:39.401036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2526
 
15.7%
i 1035
 
6.4%
a 1011
 
6.3%
, 787
 
4.9%
e 678
 
4.2%
n 550
 
3.4%
. 529
 
3.3%
t 448
 
2.8%
o 438
 
2.7%
A 406
 
2.5%
Other values (81) 7732
47.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6491
40.2%
Uppercase Letter 3569
22.1%
Space Separator 2526
 
15.7%
Decimal Number 1741
 
10.8%
Other Punctuation 1359
 
8.4%
Other Letter 243
 
1.5%
Dash Punctuation 124
 
0.8%
Other Symbol 27
 
0.2%
Math Symbol 27
 
0.2%
Close Punctuation 16
 
0.1%
Other values (2) 17
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1035
15.9%
a 1011
15.6%
e 678
10.4%
n 550
8.5%
t 448
 
6.9%
o 438
 
6.7%
d 327
 
5.0%
l 266
 
4.1%
s 250
 
3.9%
r 226
 
3.5%
Other values (17) 1262
19.4%
Uppercase Letter
ValueCountFrequency (%)
A 406
 
11.4%
C 384
 
10.8%
T 288
 
8.1%
N 278
 
7.8%
D 234
 
6.6%
S 215
 
6.0%
H 205
 
5.7%
B 168
 
4.7%
O 151
 
4.2%
F 138
 
3.9%
Other values (16) 1102
30.9%
Decimal Number
ValueCountFrequency (%)
0 299
17.2%
2 287
16.5%
1 262
15.0%
3 243
14.0%
4 183
10.5%
5 175
10.1%
１ 54
 
3.1%
7 54
 
3.1%
6 34
 
2.0%
8 28
 
1.6%
Other values (5) 122
7.0%
Other Letter
ValueCountFrequency (%)
伊 27
11.1%
熱 27
11.1%
海 27
11.1%
市 27
11.1%
岡 27
11.1%
静 27
11.1%
県 27
11.1%
豆 27
11.1%
山 27
11.1%
Other Punctuation
ValueCountFrequency (%)
, 787
57.9%
. 529
38.9%
/ 21
 
1.5%
* 12
 
0.9%
: 6
 
0.4%
# 4
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 116
93.5%
– 8
 
6.5%
Space Separator
ValueCountFrequency (%)
2526
100.0%
Other Symbol
ValueCountFrequency (%)
〒 27
100.0%
Math Symbol
ValueCountFrequency (%)
− 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Format
ValueCountFrequency (%)
​ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10060
62.3%
Common 5837
36.2%
Han 243
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1035
 
10.3%
a 1011
 
10.0%
e 678
 
6.7%
n 550
 
5.5%
t 448
 
4.5%
o 438
 
4.4%
A 406
 
4.0%
C 384
 
3.8%
d 327
 
3.3%
T 288
 
2.9%
Other values (43) 4495
44.7%
Common
ValueCountFrequency (%)
2526
43.3%
, 787
 
13.5%
. 529
 
9.1%
0 299
 
5.1%
2 287
 
4.9%
1 262
 
4.5%
3 243
 
4.2%
4 183
 
3.1%
5 175
 
3.0%
- 116
 
2.0%
Other values (19) 430
 
7.4%
Han
ValueCountFrequency (%)
伊 27
11.1%
熱 27
11.1%
海 27
11.1%
市 27
11.1%
岡 27
11.1%
静 27
11.1%
県 27
11.1%
豆 27
11.1%
山 27
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15652
97.0%
CJK 243
 
1.5%
None 209
 
1.3%
Math Operators 27
 
0.2%
Punctuation 9
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2526
 
16.1%
i 1035
 
6.6%
a 1011
 
6.5%
, 787
 
5.0%
e 678
 
4.3%
n 550
 
3.5%
. 529
 
3.4%
t 448
 
2.9%
o 438
 
2.8%
A 406
 
2.6%
Other values (61) 7244
46.3%
None
ValueCountFrequency (%)
１ 54
25.8%
８ 27
12.9%
〒 27
12.9%
５ 27
12.9%
７ 27
12.9%
３ 27
12.9%
é 15
 
7.2%
í 5
 
2.4%
CJK
ValueCountFrequency (%)
伊 27
11.1%
熱 27
11.1%
海 27
11.1%
市 27
11.1%
岡 27
11.1%
静 27
11.1%
県 27
11.1%
豆 27
11.1%
山 27
11.1%
Math Operators
ValueCountFrequency (%)
− 27
100.0%
Punctuation
ValueCountFrequency (%)
– 8
88.9%
​ 1
 
11.1%

Certification Contact
Text

Distinct21
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-03T10:36:39.597434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length61
Mean length36.52173913
Min length18

Characters and Unicode

Total characters7560
Distinct characters74
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.9%

Sample

1st row松澤　宏樹　Koju Matsuzawa - +81(0)9085642901
2nd rowLin, Jen-An Neil 林仁安 - 886-289116612
3rd rowLin, Jen-An Neil 林仁安 - 886-289116612
4th row松澤　宏樹　Koju Matsuzawa - +81(0)9085642901
5th row松澤　宏樹　Koju Matsuzawa - +81(0)9085642901
ValueCountFrequency (%)
222
17.8%
lin 83
 
6.7%
neil 83
 
6.7%
林仁安 83
 
6.7%
886-289116612 83
 
6.7%
jen-an 83
 
6.7%
松澤 27
 
2.2%
宏樹 27
 
2.2%
koju 27
 
2.2%
matsuzawa 27
 
2.2%
Other values (99) 502
40.3%
2023-08-03T10:36:39.864880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
986
 
13.0%
- 427
 
5.6%
1 418
 
5.5%
n 403
 
5.3%
6 383
 
5.1%
8 374
 
4.9%
2 364
 
4.8%
e 337
 
4.5%
i 293
 
3.9%
a 290
 
3.8%
Other values (64) 3285
43.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2384
31.5%
Decimal Number 2367
31.3%
Space Separator 1040
13.8%
Uppercase Letter 648
 
8.6%
Dash Punctuation 427
 
5.6%
Other Letter 375
 
5.0%
Other Punctuation 111
 
1.5%
Math Symbol 84
 
1.1%
Open Punctuation 62
 
0.8%
Close Punctuation 62
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 403
16.9%
e 337
14.1%
i 293
12.3%
a 290
12.2%
l 176
7.4%
o 122
 
5.1%
s 107
 
4.5%
u 105
 
4.4%
t 91
 
3.8%
m 80
 
3.4%
Other values (15) 380
15.9%
Uppercase Letter
ValueCountFrequency (%)
A 104
16.0%
J 96
14.8%
N 90
13.9%
L 83
12.8%
K 58
9.0%
B 38
 
5.9%
M 36
 
5.6%
C 35
 
5.4%
R 26
 
4.0%
T 18
 
2.8%
Other values (9) 64
9.9%
Decimal Number
ValueCountFrequency (%)
1 418
17.7%
6 383
16.2%
8 374
15.8%
2 364
15.4%
9 251
10.6%
0 209
8.8%
5 161
 
6.8%
4 95
 
4.0%
3 75
 
3.2%
7 37
 
1.6%
Other Letter
ValueCountFrequency (%)
安 83
22.1%
仁 83
22.1%
林 83
22.1%
宏 27
 
7.2%
澤 27
 
7.2%
松 27
 
7.2%
樹 27
 
7.2%
陳 6
 
1.6%
峻 6
 
1.6%
嘉 6
 
1.6%
Other Punctuation
ValueCountFrequency (%)
, 90
81.1%
. 16
 
14.4%
@ 4
 
3.6%
/ 1
 
0.9%
Space Separator
ValueCountFrequency (%)
986
94.8%
　 54
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 427
100.0%
Math Symbol
ValueCountFrequency (%)
+ 84
100.0%
Open Punctuation
ValueCountFrequency (%)
( 62
100.0%
Close Punctuation
ValueCountFrequency (%)
) 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4153
54.9%
Latin 3032
40.1%
Han 375
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 403
13.3%
e 337
 
11.1%
i 293
 
9.7%
a 290
 
9.6%
l 176
 
5.8%
o 122
 
4.0%
s 107
 
3.5%
u 105
 
3.5%
A 104
 
3.4%
J 96
 
3.2%
Other values (34) 999
32.9%
Common
ValueCountFrequency (%)
986
23.7%
- 427
10.3%
1 418
10.1%
6 383
 
9.2%
8 374
 
9.0%
2 364
 
8.8%
9 251
 
6.0%
0 209
 
5.0%
5 161
 
3.9%
4 95
 
2.3%
Other values (10) 485
11.7%
Han
ValueCountFrequency (%)
安 83
22.1%
仁 83
22.1%
林 83
22.1%
宏 27
 
7.2%
澤 27
 
7.2%
松 27
 
7.2%
樹 27
 
7.2%
陳 6
 
1.6%
峻 6
 
1.6%
嘉 6
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7131
94.3%
CJK 375
 
5.0%
None 54
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
986
 
13.8%
- 427
 
6.0%
1 418
 
5.9%
n 403
 
5.7%
6 383
 
5.4%
8 374
 
5.2%
2 364
 
5.1%
e 337
 
4.7%
i 293
 
4.1%
a 290
 
4.1%
Other values (53) 2856
40.1%
CJK
ValueCountFrequency (%)
安 83
22.1%
仁 83
22.1%
林 83
22.1%
宏 27
 
7.2%
澤 27
 
7.2%
松 27
 
7.2%
樹 27
 
7.2%
陳 6
 
1.6%
峻 6
 
1.6%
嘉 6
 
1.6%
None
ValueCountFrequency (%)
　 54
100.0%